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Quantifying Mutational Response to Track the Evolution of SARS-CoV-2 Spike Variants: Introducing a
Satyam Sangeet1, Raju Sarkar1, Saswat K Mohanty1
1Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal741246, India.
The Journal of Physical Chemistry. B
|September 30, 2022
Summary
We developed a new method, the Mutational Response Function (MRF), to track SARS-CoV-2 evolution. This approach identifies critical transitions in virus variants, aiding in predicting future mutations and understanding pathogenicity.
Area of Science:
- * Virology and evolutionary biology.
- * Statistical mechanics and machine learning applications.
- * Public health and infectious disease dynamics.
Background:
- * The emergence of SARS-CoV-2 variants necessitates understanding mutation patterns and evolutionary trajectories.
- * Distinguishing between Variants of Interest (VOIs) and Variants of Concern (VOCs) is crucial for global health surveillance.
- * Existing methods may not fully capture the dynamic nature of viral evolution.
Purpose of the Study:
- * To introduce a novel metric, the Mutational Response Function (MRF), for quantifying viral evolution.
- * To investigate the relationship between MRF changes and viral evolutionary transitions (VOI to VOC).
- * To develop a predictive model for future SARS-CoV-2 mutations using evolutionary and entropic data.
Main Methods:
- * Applied equilibrium statistical mechanics to define the Mutational Response Function (MRF).
- * Analyzed SARS-CoV-2 spike glycoprotein sequences from an evolutionary database.
- * Developed an ancestral-based machine learning model for mutation prediction.
Main Results:
- * A significant change in MRF was observed during the transition from VOI to VOC, indicative of a dynamic phase transition.
- * The machine learning model successfully predicted residues prone to future mutations.
- * Identified potential links between predicted mutations and increased viral fusogenicity and pathogenicity.
Conclusions:
- * The MRF serves as a valuable tool for tracking viral evolutionary stages and identifying alarming transitions.
- * Statistical mechanics-informed machine learning can enhance the prediction of viral mutations.
- * This approach offers a promising strategy for proactive monitoring and management of emerging infectious diseases.
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